Jeon Ji Woo, Choi Ji Wook, Shin Yonghee, Kang Taewook, Chung Bong Geun
Department of Mechanical Engineering, Sogang University, Seoul, South Korea.
Department of Mechanical Engineering, Sogang University, Seoul, South Korea; Institute of Integrated Biotechnology, Sogang University, Seoul, South Korea.
Water Res. 2025 Apr 15;274:123161. doi: 10.1016/j.watres.2025.123161. Epub 2025 Jan 17.
Microplastic (MP) pollution poses serious environmental and public health concerns, requiring efficient detection methods. Conventional techniques have the limitations of labor-intensive workflows and complex instrumentation, hindering rapid on-site field analysis. Here, we present the Machine learning (ML)-Integrated Droplet-based REal-time Analysis of MP (MiDREAM) system. Utilizing a compact peristaltic pump, the system achieved high-throughput droplet generation (> 200 Hz) while encapsulating MPs in uniform droplets (142 ± 8 μm). A high-resolution complementary metal oxide semiconductor (CMOS) sensor combined with an optimized YOLO v8 ML model was employed for real-time analysis, achieving a mean average precision (mAP) of 0.982 and an area under the curve (AUC) of 97.64 %. Comparative analysis with hemocytometer counting and surface-enhanced Raman spectroscopy (SERS) demonstrated the superior performance of the system, demonstrating high correlation (R² = 0.9965) and minimal deviation (6.36 %) from theoretical values. The system accurately classified MPs of different sizes, achieving accuracies of 95.4 %, 87.9 %, 95.3 %, 85.3 %, and 92.5 % for 3, 5, 10, 30, and 50 μm particles, respectively. Validation with real-world water samples confirmed the system adaptability, while maintaining high detection accuracy (> 90 %). The on-site field tests of MiDREAM system also demonstrated its robust performance for environmental monitoring in a variety of environments. Therefore, our portable and integrated MiDREAM system offers a promising solution for real-time environmental monitoring applications.
微塑料（MP）污染引发了严重的环境和公共卫生问题，需要高效的检测方法。传统技术存在工作流程繁琐、仪器复杂的局限性，阻碍了快速现场分析。在此，我们展示了基于机器学习（ML）的微塑料实时液滴分析（MiDREAM）系统。该系统利用紧凑的蠕动泵，在将微塑料包裹在均匀液滴（142±8μm）中的同时，实现了高通量液滴生成（>200Hz）。采用高分辨率互补金属氧化物半导体（CMOS）传感器与优化的YOLO v8机器学习模型进行实时分析，平均精度（mAP）达到0.982，曲线下面积（AUC）为97.64%。与血细胞计数器计数和表面增强拉曼光谱（SERS）的对比分析表明，该系统具有卓越性能，与理论值具有高度相关性（R² = 0.9965）且偏差极小（6.36%）。该系统能准确分类不同尺寸的微塑料，对于3、5、10、30和50μm颗粒的准确率分别达到95.4%、87.9%、95.3%、85.3%和92.5%。对实际水样的验证证实了该系统的适应性，同时保持了高检测准确率（>90%）。MiDREAM系统的现场测试也证明了其在各种环境中进行环境监测的强大性能。因此，我们的便携式集成MiDREAM系统为实时环境监测应用提供了一个有前景的解决方案。
